Multi-view Story Characterization from Movie Plot Synopses and Reviews
Sudipta Kar, Gustavo Aguilar, Mirella Lapata, Thamar Solorio
Abstract
This paper considers the problem of characterizing stories by inferring properties such as theme and style using written synopses and reviews of movies. We experiment with a multi-label dataset of movie synopses and a tagset representing various attributes of stories (e.g., genre, type of events). Our proposed multi-view model encodes the synopses and reviews using hierarchical attention and shows improvement over methods that only use synopses. Finally, we demonstrate how we can take advantage of such a model to extract a complementary set of story-attributes from reviews without direct supervision. We have made our dataset and source code publicly available at https://ritual.uh.edu/multiview-tag-2020.- Anthology ID:
- 2020.emnlp-main.454
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5629–5646
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.454
- DOI:
- 10.18653/v1/2020.emnlp-main.454
- Cite (ACL):
- Sudipta Kar, Gustavo Aguilar, Mirella Lapata, and Thamar Solorio. 2020. Multi-view Story Characterization from Movie Plot Synopses and Reviews. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5629–5646, Online. Association for Computational Linguistics.
- Cite (Informal):
- Multi-view Story Characterization from Movie Plot Synopses and Reviews (Kar et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/corrections-2024-05/2020.emnlp-main.454.pdf